phylogenetic rearrangement of streptomyces spp. on the...

Indian Journal of Biotechnology Vol 12, January 2013, pp 67-79

Phylogenetic rearrangement of Streptomyces spp. on the basis of internal

transcribed spacer (ITS) region using molecular morphometrics approach

Kaushik Bhattacharjee and S R Joshi*

Microbiology Laboratory, Department of Biotechnology and Bioinformatics North-Eastern Hill University, Shillong 793 022, India

Streptomycetes, the Gram positive bacteria commonly found in soil, are among the well known antibiotic producers of microbial world. Moreover, Streptomyces spp. produce about 75% of commercially and medically useful antibiotics. They

have provided more than half of the naturally occurring antibiotics discovered to date and continue to be screened for useful compounds. Most taxonomic and phylogenetic characterizations of Streptomyces have focused on primary DNA information targeting linear 16S rRNA and ITS sequences. However, RNA secondary structures are particularly not been used for such taxonomic studies, especially the systematics analysis based on “molecular morphometrics information” that are usually not found in the primary sequences. The molecular morphometrics approach has been employed in the present study for comparing the primary and secondary structure information of internal transcribed spacer (ITS) region of Streptomyces spp. using bioinformatics tools. It is an established fact that rRNA structure is highly conserved throughout evolution as most of the folding is functionally important despite primary sequence divergence. The analysis revealed considerable differences between

the conventional liner rRNA based phylogeny and the phylogenetic alignment using molecular morphometrics tools.

Keywords: Internal transcribed spacer (ITS), molecular morphometrics, phylogenetics, Streptomyces

Introduction Streptomycetes, the Gram positive aerobic bacteria,

ubiquitous in nature, produce a great many antibiotics

and other classes of biologically active secondary metabolites. Streptomyces spp. produce about 75% of

commercially and medically useful antibiotics1,2

.

They have provided more than half of the naturally

occurring antibiotics discovered to date and continue to be screened for useful compounds

1. For

the detection of previously unknown bioactive

microorganisms, microbial systematics can be an effective tool

3. The genus Streptomyces is

taxonomically located in the diverse bacterial order

Actinomycetales characterized by an astonishing diversity in terms of morphology, ecology,

pathogenicity, genome size, genomic G+C content,

and the number of coding sequences in the genome4-6

.

Since the International Streptomyces Project in 1964, an attempt was made to produce valid species

descriptions with at least a minimal number of

standard phenotypic criteria7. However, the criteria

turned out to be too minimal and the proliferation of

species continued without any real attempt to compare

species thoroughly with each other. Several studies

have attempted to use sequence data from variable regions of 16S rRNA and internal transcribed spacer

(ITS) region to establish taxonomic structure within

the genus, but the variation was regarded as too limited to help resolve problems of species

differentiation8,9

. Most taxonomic and phylogenetic

characterizations of Streptomyces using current

methods have focused on primary DNA information targeting linear 16S rRNA and ITS sequences

10. Some

studies have suggested that length and sequence

polymorphisms in the ITS region could be used to distinguish different species of prokaryotes to

enhance the differentiating capacity of 16S gene

analysis11

. ITS1 sequences, found in the tandem arrays of the nuclear ribosomal RNA between the

16S and 23S genes, have not been considered useful

for molecular systematics in Streptomyces genus12-14

.

But, ITS1 sequences gave reliable phylogenetic information especially if the predicted RNA

secondary structures were compared15,16

. However,

RNA secondary structures have not been particularly used for such taxonomic studies, although they can

provide meaningful information on systematics

because of their inherent prospects of providing

characteristics on “molecular morphometrics” usually not found in the primary sequences. The basic idea

behind molecular morphometrics is to use the

——————

*Author for correspondence:

Tel: +91-364-2722405; Fax: +91-364-2550076 E-mail: [email protected]

INDIAN J BIOTECHNOL, JANUARY 2013

68

molecular structures as a direct source of measurable

information. This method is based on the assumption

that secondary structure can be as significant as primary sequence in deriving phylogenetic

relationship. In other words, one can consider the

secondary-structure elements of RNA molecules, i.e.,

the helices, loops, bulges, and separating single-stranded portions, as phylogenetic characters

17 instead

of the conventional linear sequences.

Sequence variations in RNA sequences maintain base-pairing patterns that give rise to double-stranded

regions (secondary structure) in the molecule. Thus,

alignments of two sequences that specify the same

RNA molecules will show covariation at interacting base-pair positions. In addition to these covariable

positions, sequences of RNA-specifying genes may

also have rows of similar sequence characters that reflect the common ancestry of the genes. It is an

established fact that rRNA structure is highly

conserved throughout evolution as most of the folding is functionally important despite primary sequence

divergence. The present analysis presents the

molecular morphometrics based bioinformatics

approach employed for comparing the primary and secondary structure information of ITS region of

Streptomyces spp. for delineating phylogenetic

relationship.

Materials and Methods

Retrieval of Sequence, Conversion of DNA Sequence to RNA

and Kinetics Analysis

The gene sequence for ITS region between 16S and 23S rRNA of Streptomyces spp. were retrieved from

National Centre for Biotechnology Information

(http://www.ncbi.nlm.nih.gov/gen-omes/lproks.cgi)

and used for the study. Open source sequence conversion tools were used to convert the DNA

sequences to RNA sequences in upper case and lower

case format and a single ‘.fas’ file was created containing all the ITS sequences of Streptomyces spp..

Different physical and kinetics properties for these

ITS1 sequences were calculated using in silico open

source tool OligoCalc18

(Table 1).

Sequence Alignment Based on Primary Sequences, Alignment

Comparison and Phylogenic Analysis

Retrieved converted sequences were aligned using six different most popular progressive multiple

sequence alignment (MSA) algorithms, viz., ClustalW

(inbuilt in MEGA 4.1)19

, ClustalX220

, MAFFT21

, MUSCLE

22,23, Kalign

24, T-Coffee

25 and ProbCons

26

with default settings. The interleaved alignment

files were converted to FASTA format and used

to draw a threshold dot-plot two alignments using dotmatcher from EMBOSS tools

27. Visual comparison

tools SuiteMSA 2.1.0328

and AltAVisT 1.0 from

Bielefeld University Bioinformatics server

(http://bibiserv.techfak.uni-bielefeld.de/) were also used to check the differences between the alignments

in question.

The Tajima’s D test is a widely used test of neutrality in population genetics

29. The interleaved

alignment files were used to calculate the

Tajima’s statistics using the MEGA 4.1 software19

.

On the basis of similarity matrix of Tajima’s statistics, the Agglomerative Hierarchical Clustering

(AHC) of the alignment methods was performed

using XLSTAT 7.5.2. ITS sequences already aligned with different progressive multiple

sequence alignment methods were converted to

MEGA format and used for construction of phylogenetic trees that were inferred using neighbor-

joining30

, maximum parsimony31

and maximum

likelihood32

methods using MEGA 4.1 software. The

stability of relationships was assessed by performing bootstrap analysis of 1,000 replicates.

Prediction of Secondary Structure of RNA and Analysis

For prediction of RNA’s secondary structure

from its sequence, free energy minimization with

energy parameters based on the nearest-neighbor model and comparative analysis are the primary

methods33

. Restrictions and constraints of the

RNA sequences of the selected Streptomyces spp. were checked and secondary folding were predicted

using MFOLD 3.034

at a default temperature of 37ºC,

[Na+=1 M] and [Mg

++=0 M]. The structure chosen

from different output files was the ones with the highest negative free energy value but seeking the

one essentially identical for all the species (ring

model structure). The ‘.ct’ files and Vienna files were downloaded and used for secondary structure

display, and loop free-energy decomposition (LFD)

and minimum free energy (MFE) determination purpose. The energy dot-plots

35 of the sequences

were also drawn using the MFOLD 3.034

. Structure

annotation followed in the study was according

to Zuker and Jacobson method36

. The maximum weight matching (MWM) analysis and circle plot of

individual RNA sequences were drawn using Circle

0.1.1 package37,38

, which gives the assumptions regarding the pseudoknots.

BHATTACHARJEE & JOSHI: MOLECULAR MORPHOMETRICS OF STREPTOMYCES SPP.

69


70

Sequence Alignment Based on Secondary Structure and Phylogenetic Analysis

The RNA sequences were folded and aligned, taking structural similarity in account and RNA free-energy model in parallel using the LocARNA 1.5.2

39,

which was achieved with Sankoff-style algorithms. This analysis was based on RIBOSUM85_60 model and other default scoring parameters. The structure annotated alignment, RNAalifold consensus structure and consensus minimum free energy (MFE) was also interpreted by LocARNA 1.5.2

39. The structure

annotated phylogenetic tree file in ‘tree’ format generated by the LocARNA 1.5.2 was used for construction of phylogenetic tree using TreeDyn 198.3

40. Branches with bootstrap value lower than

90 were collapsed and in each case the branch length were also displayed on each branch.

Results and Discussion

Sequence Retrieval, Conversion and Kinetics Analysis

After conversion of the retrieved DNA sequences of ITS1 between 16S and 23S rRNA of Streptomyces spp. to RNA, different physical and kinetics properties were calculated at standard condition (Table 1). Pearson's correlation test between GC% and melting temperature (Tm) of the RNA sequences shows a significant correlation (P<0.05) (Fig. 1) between them and they are linearly correlated (F=259.84, P< 0.0001) (Fig. 2). This kinetics analysis revealed the consistency of the selected RNA sequences of the ITS1.

Sequence Alignment Based on Primary Sequences, Alignment

Comparison and Phylogenetic Analysis

The sequence alignments based on primary sequence or linear sequence of the ITS1 using different multiple sequence alignment (MSA) algorithms showed a considerable amount of variability. The differences between the alignments were visible in the generated dot-plots (Fig. 3), which was also supported by the visual comparisons.

Earlier, studies have shown that bacterial

communities are neutrally evolved, especially in case

of members of the same genus41-44

. In the present

study, the neutrality test of molecular evolution of the population was interpreted with the Tajima’s D

(Table 2). But only ClustalX2, Kalign and ProbCons

Fig. 1—Scatter diagram of GC% and melting temperature (Tm) of the RNA sequences (P<0.05) contributed by 28 ITS1 sequences of Streptomyces spp.

Fig. 2—Regression line of GC% and melting temperature (Tm) of the RNA sequences (P<0.05) ITS1 sequences of Streptomyces spp.

Table 2—Tajima's D statistics for 28 ITS1 sequences of Streptomyces spp.

ClustalW-MEGA ClustalX2 Kalign MAFFT MUSCLE T-Coffee ProbCons

m* 28 28 28 28 28 28 28

S 96 89 109 119 98 99 104

Ps 0.53631 0.48901 0.56477 0.58333 0.51309 0.52381 0.530612

Θ 0.13782 0.12566 0.14513 0.1499 0.13185 0.1359 0.136353

π 0.21619 0.17299 0.21821 0.23941 0.20361 0.20813 0.191327

D 2.19493 1.45073 1.949399 2.31606 2.10166 2.06551 1.559185

*(m = No. of sites, S = No. of segregation sites, Ps = S/m, Θ = Ps/a1, π = Nucleotide diversity, D = Thajima's test statistics)


71

Fig. 3—Threshold dot-plot between seven different multiple sequence alignments of the 16S-23S ITS1 region of Streptomyces spp.

showed a significant value for this ITS1 based analysis. ProCons, an algorithm primarily written for

protein sequences, was found to give a significant

Tajima’s D value (Table 2). The best Tajima’s D value was presented by ClustalX2 (D=1.450725).

Agglomerative hierarchical clustering (AHC) based on

similarity matrix of the Tajima’s statistics interpreted

a close relationship between Kalign and ProbCons, and between MUSCLE and T-Coffee (Fig. 4). The

progressive multiple sequence alignment algorithm

ClustalX2 showed the best possible result supporting neutral theory of molecular evolution

45 based on ITS1

sequences of the Streptomyces spp. from among the

seven approaches adopted in the present study. The phylogenetic trees constructed using seven

different algorithms by the neighbor-joining method

and comparing the ITS1 sequences retrieved showed

a variation in the clustering of selected isolates because of their differences in alignment algorithm

(Fig. 5). Moreover, the current phylogenetic tree

reconstructions can not infer a single underlying tree topology for each informative site along a

sequence. This ultimately leads to overestimation

of phylogenetic distances giving misleading results.

Fig. 4—Dendogram showing the relationship between the different sequence alignments algorithms based on agglomerative

hierarchical clustering (AHC) of similarity matrix of the Tajima’s statistics.


72

Fig. 5—Unrooted phylogenetic tree constructed from seven different multiple sequence alignments of the 16S-23S ITS1 region of Streptomyces spp. Sequence distances were established by using the neighbour-joining method. Bootstrapping values (from 1000 bootstrap trials) are given at respective nodes. GenBank accession numbers for the sequences used to construct this tree are given in Table 1. (Bar=0.05 nucleotide substitutions per position)


73

Fig. 6 Contd.


74

Contd. Fig. 6

Fig. 6—Predicted ITS1 RNA secondary structures for 28 species of the genus Streptomyces spp. and their structure formation enthalpies according to MFOLD.

Therefore, we propose that the use of ITS1 liner

sequences in case of Streptomyces for phylogenetic

tree inference is questionable, at least in the case of linear DNA or RNA sequences as supported by

some studies14,46,47

.

Prediction of Secondary Structure of RNA and Analysis

Initially, twenty eight predicted RNA secondary

structures were reconstructed to provide basic

information (Fig. 6). The secondary structural features of ITS1 regions as shown in the figures were analysed

based on the conserved stems and loops in all the

isolates. The ITS1 sequences exhibited the highly

conserved six-helicoidal ring-model structure.

Sequence Alignment Based on Secondary Structure and

Phylogenic Analysis

The structure annotated alignment of the ITS1 sequences of the Streptomyces spp. and RNAalifold

consensus structure showed structurally conserved

regions (Figs 7 & 8). The interpreted MFE was

-169.13 kcal/mol, which is much lower than all the calculated dG of the individual ITS1 sequences

that indicated more stable and accepted secondary


75


76

Fig. 7—The structure annotated alignment of 28 of ITS1 sequences of Streptomyces spp. aligned by taking structural similarity in account and RNA free-energy model in parallel using the LocARNA 1.5.2.


77

Fig. 8—RNAalifold consensus structure of 28 of ITS1 sequences of Streptomyces spp.

structure. The structure annotated phylogenetic tree (Fig. 9) when rearranged provided a better evolutionary distance of the Streptomyces spp. in question. In this case, phylogenetic tree branches with bootstrap value lower than 50 were collapsed and not displayed for further phylogenetic affiliation. Molecular morphometrics and sequence comparison differ mainly on a methodological point in a nucleotide sequence, where the structural polymorphism appears as size variations, mostly in regions where insertion/deletion events take place, although point mutations can also lead to structural polymorphism. In other words, these regions are those in which the multiple sequence comparison programs lead to poorly reliable alignments, as the signal/noise ratio they provide is too low

48,49. The secondary

structures, which although depend on the primary nucleotide sequence, can in fact be considered as a distinct set of informative characters providing their own phylogenetic signals. In particular, the ITS1 sequences and predicted RNA secondary structures have afforded a new view of the phylogeny of the genus Streptomyces.

Fig. 9—The structure annotated phylogenetic tree of the Streptomyces spp. on the basis of molecular morphometrics data. The bootstrap value lower than 90 were collapsed and not displayed

in the tree. (Bar=0.5 nucleotide substitutions per position)

Conclusion

It is an established fact that rRNA structure

is highly conserved throughout evolution as most of

the folding is functionally important despite primary sequence divergence in case of ITS1

sequences of Streptomyces spp.. The present analysis,

however, reveales considerable variation between the conventionally used linear DNA based phylogeny

and the phylogenetic alignment derived using

molecular morphometrics tools. The superiority of RNA secondary based phylogeny over DNA or

RNA linear sequence phylogeny is also evident from

the present analysis.

Acknowledgement

Financial support received from the Department of

Information Technology (Ministry of Communication

and Information Technology), Government of India, New Delhi is gratefully acknowledged. Authors are

thankful to the Mr Shakti Kumar and Mr Graciously

Kharumniud, Bioinformatics Centre (BIC), NEHU, Shillong, for their help extended during the study.

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